last time

TEMPLATE BULLSHIT

TO EDIT

ADD DATA SOURCES!!!!!!!!!!!!!!!

Chart 0

Chart 1

Cars

mpg cyl disp hp drat wt qsec vs am gear carb
Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2

isochrones

Column

Approach

The gtfsrouter package allows us to calculate all stations reachable within a specified time period from a nominated station (isochrones). We use the hull polygon as an indicator for the city area reachable.

See below for a isochrone plot of a 30min Monday work–home trip starting from Helmholtzstraße at 18:00.

We try to simulate a home–work trip in the morning rush hour arriving at 08:00. Assuming a five minute walk at beginning and end of the trip, we limit the GTFS data to Monday (2021-01-18) and calculate the isochrones for a 40min time range. By that we want to get all reachable stations by local transport (< 60min, PBefG §8 (1)) and rate the station inside the network.

  • reachablity of possible work places
  • calculation with all transport agencies – domination of the hull area in 40 min time range by reachable fanning out long-distance transport
  • no weighting of the reachable area

30min IC home trip (18:00) from Helmholtzstr.

Column

area size of the hull enclosing the routed points

traveltimes to center

Column

approach

The local transport plan (p. 106) sets targets for the connectivity standards. Different categories of center areas (see StEP, p. 45) have to be reachable within a certain time and with a maximum number of transfer. This should hold for 95% of the stations.

Based on the GTFS data, we try to recreate the result of the monitoring (NVP Anlage 1, p. 12), mentioning a degree of fulfillment of 99,7% for the central areas.

  • destination:
    • City West (Zoo/ Kurfürstendamm)
    • Mitte (Potsdamer Platz/ Alexanderplatz)
  • max. tt: 60min
  • max. transfers: 2

From a more detailed display of the area (p. 39), we create a shape file enclosing the associated stations. The tidytransit package let’s us calculate the shortest travel time for all stations to any of a specified set of stations. For that arrival has to be set to TRUE.

We try to simulate a home–work trip in the morning rush hour arriving at 08:00. Assuming five minute walking at beginning and end of the trip, we limit the GTFS data to Monday (2021-01-18) and calculate the shortest travel times in a 90min time range ending at 07:55.

Percent of all berlin stations fullfill the connectivity standard according to the GTFS data

98.99 %

Column

shortest travel time to one of the stations inside City West or Mitte


https://rstudio.github.io/leaflet/

  • Interactive panning/zooming

  • Compose maps using arbitrary combinations of map tiles, markers, polygons, lines, popups, and GeoJSON.

  • Create maps right from the R console or RStudio

  • Embed maps in knitr/R Markdown documents and Shiny apps

  • Easily render Spatial objects from the sp package, or data frames with latitude/longitude columns

  • Use map bounds and mouse events to drive Shiny logic

---
title: "Data Science Transport – Second Assignment – Group 12"
output: 
  flexdashboard::flex_dashboard:
    orientation: columns
    vertical_layout: fill
    source_code: embed
---

```{r setup, include=FALSE}
library(flexdashboard)
library(gtfsrouter)
library(tidyverse)
library(tidytransit)
library(sf)
library(tmap)
library(units)
library(RColorBrewer)
tmap_mode("view")
```

last time {data-icon="fa-hourglass-half"}
=====================================
TEMPLATE BULLSHIT

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ADD DATA SOURCES!!!!!!!!!!!!!!!

- test1
- test2

### Chart 0
```{r}

library(leaflet)
leaflet() %>%
  addTiles() %>%
  addMarkers(lng=174.768, lat=-36.852, popup="The birthplace of R")
```

### Chart 1

```{r}
# 1. Plot the dots themselves
```

### Cars

```{r}
knitr::kable(mtcars)
```

isochrones {data-icon="fa-expand-arrows-alt"}
=====================================

```{r, include = FALSE}
##############################################################
#
#   READ GTFS DATA
#
##############################################################
# set work directions
setwd_gtfs <- function(){setwd("~/Documents/Uni/Master/DataScienceTransport/data/vbb-gtfs")}
setwd_data <- function(){setwd("~/Documents/Uni/Master/DataScienceTransport/data")}
setwd_work <- function(){setwd("~/Documents/Uni/Master/DataScienceTransport/assignment_2")}

setwd_work

# read gtfs data for monday
file <- file.path("~/Documents/Uni/Master/DataScienceTransport/data/vbb-gtfs/2020-12_2020-12-28.zip")
gtfs <- extract_gtfs(file) %>% gtfs_timetable(day = 2)

##############################################################
#
#   SET TIMES
#
##############################################################
start_time <- 7 * 3600 + 1200
end_time <- 8 * 3600

# create isochrone
# ic <- gtfs_isochrone (gtfs,
#                       from = from,
#                       start_time = start_time,
#                       end_time = end_time)

##############################################################
#
#   CREATE STOPS SF OBJECT
#
##############################################################
stops <- st_as_sf(gtfs$stops,
                   coords = c("stop_lon", "stop_lat"),
                   crs = 4326) %>% 
  st_transform(25833)

##############################################################
#
#   SHAPE DISTRICTS NEW (+ area)
#
##############################################################
setwd_data()
shape_districts_new <- read_sf(dsn = "LOR_SHP_2019-1", layer = "Planungsraum_EPSG_25833")
setwd_work()

shape_districts_new <- shape_districts_new %>% 
  group_by(BEZIRK) %>% 
  summarise() %>% 
  filter(!is.na(BEZIRK)) %>% 
  rename(NAME = BEZIRK) %>% 
  mutate(AREA = st_area(geometry)) %>% 
  select(NAME, AREA, everything()) %>% 
  mutate(AREA = (AREA / 1000000) * as_units("km2"))

# setting crs of polygons
st_crs(shape_districts_new$geometry) <- 25833

shape_berlin <- st_union(shape_districts_new)

##############################################################
#
#   SPECIFIC SHAPES AND STOPS
#
##############################################################

stops_in_berlin <- stops %>% 
  mutate(inside_berlin = st_within( geometry, shape_berlin )) %>% 
  mutate(inside_berlin = !is.na( as.numeric( inside_berlin ))) %>% 
  filter(inside_berlin == TRUE)

# get isochrone area
# ic = gtfs_isochrone (gtfs,
#                      from = "Berlin, Sowjetisches Ehrenmal",
#                      #from_is_id = TRUE,
#                      start_time = start_time,
#                      end_time = end_time)$hull$area

##############################################################
#
#   CALCULATE ISOCHRONES
#
##############################################################
# # the following code calculates the isochrones (inefficent, ~ 15h)
# # instead of running the code, we read in the pre-calculated file

# stops_ic_area <- vector(mode = "double")
# 
# # create isochrone areas for stops in 50 minutes
# for (stop_name in stops$stop_name){
# 
#   tryCatch( {
#     ic_area <- gtfs_isochrone (gtfs,
#                                from = stop_name,
#                                #from_is_id = TRUE,
#                                start_time = start_time,
#                                end_time = end_time)$hull$area
#     if(is.null(ic_area)) {
#       stops_ic_area <<- rbind(stops_ic_area, 0)
#       print(paste(stop_name, ": ", ic_area, "!!!!!!!!!!"))
#     } else {
#       stops_ic_area <<- rbind(stops_ic_area, ic_area)
#       print(paste(stop_name, ": ", ic_area))
#     }
#     },
#     error = function(e) {
#       stops_ic_area <<- rbind(stops_ic_area, 0)
#       print(paste("ERROR!!!", stop_name))
#       }
#     )
# }
# 
# ##############################################################
# #
# #   CLEANING
# #
# ##############################################################
# 
# # merge and clean
# # https://r-spatial.github.io/sf/reference/bind.html
# # https://cran.r-project.org/web/packages/units/vignettes/units.html
# rownames(stops_ic_area) <- NULL
# stops_area <- st_sf(data.frame(stops, stops_ic_area / 1000000)) %>%
#   rename(ic_area = stops_ic_area.1e.06,
#          id = stop_id,
#          name = stop_name,
#          parent = parent_station) %>% 
#   select(id, name, parent, ic_area) %>% 
#   mutate(ic_area = ic_area * as_units("km2"))
# 
# # save
# # https://r-spatial.github.io/sf/reference/st_write.html
# st_write(stops_area, "output_stops_ic_area.shp")
stops_area <- st_read("output_stops_ic_area.shp")

# more cleaning for plot
# https://dplyr.tidyverse.org/reference/distinct.html
stops_area = 
  stops_area %>% 
  select(name, ic_area) %>% 
  distinct(name, .keep_all = TRUE)

stops_area_berlin <- stops_area %>% 
  mutate(inside_berlin = st_within( geometry, shape_berlin )) %>% 
  mutate(inside_berlin = !is.na( as.numeric( inside_berlin ))) %>% 
  filter(inside_berlin == TRUE) %>% 
  select(-inside_berlin) %>% 
  mutate(id = paste(name, ": ", round(ic_area)))
```

Column {data-width=100}
-------------------------------------
    
### Approach {data-height=200}

The [gtfsrouter](https://atfutures.github.io/gtfs-router/) package allows us to calculate all stations reachable within a specified time period from a nominated station ([isochrones](https://atfutures.github.io/gtfs-router/reference/gtfs_isochrone.html)). We use the hull polygon as an indicator for the city area reachable.

See below for a isochrone plot of a 30min Monday work–home trip starting from Helmholtzstraße at 18:00.

We try to simulate a home–work trip in the morning rush hour arriving at 08:00. Assuming a five minute walk at beginning and end of the trip, we limit the GTFS data to Monday (2021-01-18) and calculate the isochrones for a 40min time range. By that we want to get all reachable stations by local transport (< 60min, [PBefG §8 (1)](https://www.gesetze-im-internet.de/pbefg/__8.html)) and rate the station inside the network.

- reachablity of possible work places
- calculation with all transport agencies
-- domination of the hull area in 40 min time range by reachable fanning out long-distance transport
- no weighting of the reachable area

### 30min IC home trip (18:00) from Helmholtzstr. {data-height=100}
    
```{r}
ic_einstein <- gtfs_isochrone(gtfs,
                              from = "Berlin, Helmholtzstr.",
                              start_time = 18 * 3600,
                              end_time = 18 * 3600 + 1800)

tm_basemap(leaflet::providers$OpenStreetMap.DE) +
  tm_shape(ic_einstein$hull) + 
  tm_polygons(col = "red",
              alpha = 0.2,
              border.col = "red") +
  tm_shape(ic_einstein$routes) +
  tm_lines() +
  tm_shape(ic_einstein$end_points) +
  tm_dots(col = "red") + 
  tm_shape(ic_einstein$start_point) + 
  tm_dots(col = "green")
```

Column {data-width=300}
-------------------------------------
   
### area size of the hull enclosing the routed points

```{r}
##############################################################
#
#   PLOT
#
##############################################################

tm_shape(shape_districts_new) +
  tm_polygons(alpha = 0,
              popup.vars = c("area" = "AREA")) +
  tm_shape(stops_area_berlin) +
  tm_dots(col = "ic_area",
          id = "name",
          popup.vars = c("area" = "ic_area"),
          size = 0.07,
          border.lwd = 0.3,
          legend.hist = TRUE,
          n = 15,
          title = "isochrone area [km^2]") +
  tm_view(bbox = shape_berlin)
```


traveltimes to center {data-icon="fa-stopwatch"}
=====================================

```{r, include = FALSE}
##############################################################
#
#   SHAPE CENTER AREAS
#
##############################################################
# "Zentrentragender Stadtraum mit höchster / hoher Urbanität"
# of Zentrumsbereichskernen
# see page 39: https://www.stadtentwicklung.berlin.de/planen/stadtentwicklungsplanung/download/zentren/2011-07-31_StEP_Zentren3.pdf
# or page 45 (less detailed): https://www.stadtentwicklung.berlin.de/planen/stadtentwicklungsplanung/download/zentren/StEP_Zentren_2030.pdf
# recreated with QGis

shape_center <- read_sf(dsn = "shape_center_areas", layer = "center_areas") %>% 
  mutate(name = c("east", "west")) %>% 
  select(name)

shape_center_east <- shape_center %>% filter(name == "east")
shape_center_west <- shape_center %>% filter(name == "west")

##############################################################
#
#   READ GTFS DATA
#
##############################################################
# now we work with tidytransit
# calculation of shortest tt from all station to specific ones is more convinent

setwd_gtfs()
gtfs <- read_gtfs("2020-12_2020-12-28.zip")
setwd_work()

# http://tidytransit.r-transit.org/reference/filter_stop_times.html
stop_times_filtered <- filter_stop_times(gtfs, "2021-01-18", "06:00:00", "07:55:00")

##############################################################
#
#   GET STOPS
#
##############################################################
stops <- st_as_sf(gtfs$stops, coords = c("stop_lon", "stop_lat"), crs = 4326) %>%
  st_transform(25833) %>% 
  select(stop_name) %>%
  rename(name = stop_name) %>%
  distinct(name)

stops_berlin <- stops %>% 
  mutate(inside_berlin = st_within( geometry, shape_berlin )) %>% 
  mutate(inside_berlin = !is.na( as.numeric( inside_berlin ))) %>% 
  filter(inside_berlin == TRUE) %>% 
  select(name)

stops_center <- stops %>% 
  mutate(inside_center = st_within( geometry, shape_center )) %>% 
  mutate(inside_center = !is.na( as.numeric( inside_center ))) %>% 
  filter(inside_center == TRUE) %>% 
  select(name)

stops_center_east <- stops %>% 
  mutate(inside_center_east = st_within( geometry, shape_center_east )) %>% 
  mutate(inside_center_east = !is.na( as.numeric( inside_center_east ))) %>% 
  filter(inside_center_east == TRUE) %>% 
  select(name)

stops_center_west <- stops %>% 
  mutate(inside_center_west = st_within( geometry, shape_center_west )) %>% 
  mutate(inside_center_west = !is.na( as.numeric( inside_center_west ))) %>% 
  filter(inside_center_west == TRUE) %>% 
  select(name)

##############################################################
#
#   TT calculation
#
##############################################################
# what are the tt to the center areas?
# according to Nahverkehrsplan Berlin 2019-2023: ANlage 1 - Monitoringbericht (p. 12)
# standard: tt_max = 3600, n_transfer_max = 2, n_realise_stations = 0.95

tt <- travel_times(
  stop_times_filtered,
  stops_center$name,
  time_range = 5400,
  arrival = TRUE,
  max_transfers = 2,
  # max_departure_time = NULL,
  return_coords = TRUE,
  return_DT = FALSE
)

# clean it for plot
tt <- tt %>% 
  rename(from = from_stop_name,
         to = to_stop_name,
         tt = travel_time,
         departure = journey_departure_time,
         arrival = journey_arrival_time
         ) %>% 
  select(-c(from_stop_id, to_stop_id, to_stop_lat, to_stop_lon)) %>% 
  st_as_sf(coords = c("from_stop_lon", "from_stop_lat"),
           crs = 4326) %>% 
  st_transform(25833) %>% 
  mutate(tt = set_units(round(tt/60, 2), "min"))
```

Column {data-width=100}
-------------------------------------
    
### approach {data-height=400}

The [local transport plan](https://www.berlin.de/sen/uvk/verkehr/verkehrsplanung/oeffentlicher-personennahverkehr/nahverkehrsplan/) (p. 106) sets targets for the connectivity standards. Different categories of center areas (see [StEP](https://stadtentwicklung.berlin.de/planen/stadtentwicklungsplanung/de/zentren/zentren2030/index.shtml), p. 45) have to be reachable within a certain time and with a maximum number of transfer. This should hold for 95% of the stations.

Based on the GTFS data, we try to recreate the result of the monitoring ([NVP Anlage 1](https://www.berlin.de/sen/uvk/_assets/verkehr/verkehrsplanung/oeffentlicher-personennahverkehr/nahverkehrsplan/broschure_nvp_2019_anlage_1.pdf), p. 12), mentioning a degree of fulfillment of 99,7% for the central areas.

* destination:
  + City West (Zoo/ Kurfürstendamm)
  + Mitte (Potsdamer Platz/ Alexanderplatz)
* max. tt: 60min
* max. transfers: 2

From a more detailed [display of the area](https://www.stadtentwicklung.berlin.de/planen/stadtentwicklungsplanung/download/zentren/2011-07-31_StEP_Zentren3.pdf) (p. 39), we create a shape file enclosing the associated stations. The [tidytransit](https://tidytransit.r-transit.org/) package let's us [calculate](https://tidytransit.r-transit.org/reference/travel_times.html) the shortest travel time for all stations to any of a specified set of stations. For that `arrival` has to be set to `TRUE`.

We try to simulate a home–work trip in the morning rush hour arriving at 08:00. Assuming five minute walking at beginning and end of the trip, we limit the GTFS data to Monday (2021-01-18) and calculate the shortest travel times in a 90min time range ending at 07:55.

### Percent of all berlin stations fullfill the connectivity standard according to the GTFS data {data-height=200}

```{r}
##############################################################
#
#   DEGREE OF FULLFILMENT
#
##############################################################

n_of_stations <- tt %>%
  mutate(inside_berlin = st_within( geometry, shape_berlin )) %>% 
  mutate(inside_berlin = !is.na( as.numeric( inside_berlin ))) %>% 
  filter(inside_berlin == TRUE) %>% 
  mutate(outside_center = st_within( geometry, shape_center )) %>% 
  mutate(outside_center = is.na( as.numeric( outside_center ))) %>% 
  filter(outside_center == TRUE) %>%
  nrow()

n_of_stations_valid <- tt %>% 
  mutate(inside_berlin = st_within( geometry, shape_berlin )) %>% 
  mutate(inside_berlin = !is.na( as.numeric( inside_berlin ))) %>% 
  filter(inside_berlin == TRUE) %>% 
  mutate(outside_center = st_within( geometry, shape_center )) %>% 
  mutate(outside_center = is.na( as.numeric( outside_center ))) %>% 
  filter(outside_center == TRUE) %>%
  filter(tt <= 60 * as_units("min")) %>% 
  filter(transfers <= 2) %>% 
  nrow()

percent_stations_valid <- n_of_stations_valid / n_of_stations * 100
percent_stations_valid <- round(percent_stations_valid, 2)

valueBox(paste(percent_stations_valid, "%"), icon = "fa-crosshairs")
```
    

Column {data-width=300}
-------------------------------------
   
### shortest travel time to one of the stations inside City West or Mitte

```{r}
##############################################################
#
#   PLOT
#
##############################################################

# https://campus.datacamp.com/courses/visualizing-geospatial-data-in-r/raster-data-and-color?ex=9
rdylgn <- rev(brewer.pal(7, "RdYlGn"))

# https://leaflet-extras.github.io/leaflet-providers/preview/
# https://tlorusso.github.io/geodata_workshop/tmap_package
# https://www.rdocumentation.org/packages/tmap/versions/3.0/topics/tm_basemap
# https://rdrr.io/cran/tmap/man/tm_view.html
# https://leafletjs.com/reference-1.3.4.html#map-methods-for-modifying-map-state

tm_basemap(leaflet::providers$CartoDB.DarkMatter) +
  tm_shape(shape_districts_new) + 
  tm_polygons(alpha = 0,
              lwd = 1.5,
              border.col = "white",
              popup.vars = c("area" = "AREA")
              ) +
  tm_shape(shape_center) +
  tm_polygons(alpha = 0.2,
              col = "red",
              border.col = "red"
              ) + 
  tm_shape(tt) +
  tm_dots(col = "tt",
          style = "fixed",
          breaks = c(0, 10, 20, 30, 40, 50, 60, 120),
          labels = c("0 – 10", "10 – 20", "20 – 30", "30 – 40", "40 – 50", "50 – 60", "> 60"), 
          id = "from",
          palette = rdylgn,
          title = "traveltime [min]",
          popup.vars = c("to" = "to", 
                         "traveltime" = "tt",
                         "departure at" = "departure",
                         "arrival at" = "arrival",
                         "number of transfers" = "transfers")
          ) +
  tm_view(bbox = shape_center)
```


***

https://rstudio.github.io/leaflet/

- Interactive panning/zooming

- Compose maps using arbitrary combinations of map tiles, markers, polygons, lines, popups, and GeoJSON.

- Create maps right from the R console or RStudio

- Embed maps in knitr/R Markdown documents and Shiny apps

- Easily render Spatial objects from the sp package, or data frames with latitude/longitude columns

- Use map bounds and mouse events to drive Shiny logic